Systems, devices, and methods for determining a degree of respiratory effort exerted by a patient while breathing and/or determining a respiratory effort score for a patient

ABSTRACT

The present invention is a respiratory monitoring device which uses 2+ sensors to map respiratory motion in patients to interpret into a respiratory effort and severity score. The core components of the invention are contact-based sensors that measure relative motion of the chest, abdomen, and/or other key anatomical features, a processing unit which takes in the data from all sensors, an algorithm that analyzes and compares the data from each sensor to understand relative motion and interpret it into clinically-relevant information, and a display screen that shares this information with clinicians. The sensors are connected to each other and the information processing unit which shares data with the screen for display of a respiratory severity score based on analysis of Thoraco-Abdominal Asynchrony (TAA) or similar indicators of respiratory effort as measured by the sensor network and analyzed by the algorithm.

RELATED APPLICATIONS

The present application is a CONTINUATION of International ApplicationNumber PCT/US2020/063458, filed Dec. 4, 2020, which is an INTERNATIONALAPPLICATION (PCT) of U.S. Provisional Patent Application No. 62/944,355,filed on 5 Dec. 2019 and entitled “RESPIRATORY SEVERITY ASSESSMENT USINGMOTION-BASED SENSING” and U.S. Provisional Patent Application No.63/094,056, filed on 20 Oct. 2020 and entitled “SYSTEMS, DEVICES, ANDMETHODS FOR THORACOABDOMINAL ASYNCHRONY-BASED RESPIRATORY EFFORTASSESSMENT IN PATIENTS,” both of which are incorporated, in theirentireties, herein.

BACKGROUND

Respiratory diseases are a major global cause of morbidity and mortalityin children and adults. These illnesses include Respiratory DistressSyndrome (RDS), Acute Respiratory Distress Syndrome (ARDS), PediatricAcute Respiratory Distress Syndrome (PARDS), asthma, and upper and lowerrespiratory tract infections, such as croup, bronchiolitis andpneumonia. Among pediatric intensive care unit (PICU) patients notadmitted for respiratory illness, respiratory distress is of greatconcern because unrecognized respiratory failure is the leading cause ofcardiopulmonary arrest in infants; and respiratory arrest is a majorcontributor to adult mortality. Early recognition and treatment arecritical to reducing morbidity and mortality. Thus, respiratorymonitoring to ensure appropriate utilization of respiratory support is acritical area of focus for general and ICU clinicians.

Traditionally, respiratory effort exerted by patients has been assessedusing both direct and indirect methods. The most direct assessment ofrespiratory effort is the calculation of work of breathing, or overallenergy expenditure associated with respiration, which may be calculatedas the integral of the product of respiratory volume and pressure.Esophageal manometry, defined as pressure measured by a balloon catheterplaced in a patient's esophagus, is considered a gold standard forminimally invasive, quantitative assessment of respiratory effortthrough work of breathing calculation; however, it is not widely adoptedin clinical practice due to poor interpretability by clinicians.

Less direct approaches to measure work of breathing rely on assessingconditions such as labored breathing or respiratory distress, ordyspnea, while the patient is at rest, the patient's use of accessoryrespiratory muscles, and measuring paradoxical motion of the patient'sabdomen in qualitative or semiquantitative ways. One example of anexisting clinical standard for objective clinical assessment ofrespiratory distress in children and infants known as the SilvermanAndersen respiratory severity score (RSS). The RSS is a semiquantitativeassessment of five parameters correlated with work of breathing that hasbeen pioneered for use in low-resource settings. RSS scores range from 0to 10 based on the summed severity grades of five parameters said to beat grade 0, 1, or 2. However, as with many clinical assessmentguidelines, this metric suffers from poor interobserver variabilitywhich may only be rectified by continuous, extensive training for theretention of assessment skills. In addition, this assessment relies onthe availability and direct observation of medical professionals anddoes not allow for continuous monitoring, potentially compromising theability to detect increased breathing effort at its onset and intervenein a timely manner.

FIG. 1 provides a table of tools currently available that indirectlyassess a degree of effort a patient exerts to breathe via mechanical,acoustic, and/or electrical sensing devices along with a description oftheir respective functions and limitations. While each of these toolshas the ability to measure one or multiple signatures of breathingeffort, each has unique limitations related to accuracy, ability to tiemonitored data to breathing effort, and commercial availability thatlimit their respective usefulness and accuracy, particularly in aclinical setting.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 provides a table of tools currently available that indirectlyassess a degree of effort a patient exerts to breathe via mechanical,acoustic, and/or electrical sensing devices along with a description oftheir respective functions and limitations, in accordance withembodiments of the present invention;

FIG. 2A provides a graph that shows a first chest signal aligned in timewith a first abdominal signal, in accordance with embodiments of thepresent invention;

FIG. 2B provides a graph that shows a second chest signal aligned intime with a second abdominal signal, in accordance with embodiments ofthe present invention;

FIG. 2C provides a graph that shows a third chest signal aligned in timewith a third abdominal signal, in accordance with embodiments of thepresent invention;

FIG. 3A presents an exemplary system that may be configured to executeone or more methods disclosed herein, in accordance with embodiments ofthe present invention;

FIG. 3B is a block diagram showing an exemplary computer system, inaccordance with embodiments of the present invention;

FIG. 4A is an illustration of an exemplary sensor array that includesthree sensor modules, in accordance with embodiments of the presentinvention;

FIG. 4B is an illustration of another exemplary sensor array, inaccordance with embodiments of the present invention;

FIG. 4C provides an exploded view of sensor array, in accordance withembodiments of the present invention;

FIG. 4D is an illustration of a patient with a sensor array positionedthereon, in accordance with embodiments of the present invention;

FIG. 5A is a flowchart showing exemplary steps of a process fordetermining a patient's respiratory rate, in accordance with embodimentsof the present invention;

FIG. 5B depicts a graph of a waveform that may represent received sensordata, in accordance with embodiments of the present invention;

FIG. 6 is a flowchart showing exemplary steps of a process fordetermining a patient's TAA, a degree of respiratory distress exhibitedby the patient, and/or a respiratory distress score for the patient, inaccordance with embodiments of the present invention;

FIG. 7 is a flowchart showing exemplary steps of another process fordetermining a patient's TAA, a degree of respiratory distress exhibitedby the patient, and/or a respiratory distress score for the patient, inaccordance with embodiments of the present invention;

FIG. 8 provides a graph wherein a phase shift Ø between the Hilberttransform-filtered amplitude of data received from a sensor placedproximate to the patient's navel or abdomen, in accordance withembodiments of the present invention;

FIG. 9 provides a graph that shows a motion capture test, in accordancewith embodiments of the present invention;

FIG. 10 is a flowchart showing exemplary steps of a process forgathering information regarding movements of a patient's thorax andportions thereof while breathing over time, in accordance withembodiments of the present invention;

FIG. 11 provides an example of an image that may be received in stepwhere different markers, in the form of dots drawn on the patient andtabs sticking up from the patient delineate different positioned on thepatient's thorax, or chest, in accordance with embodiments of thepresent invention;

FIG. 12 provides an exemplary graph showing three Lissajous curves thatplot abdominal movement as a function of rib cage movement, inaccordance with embodiments of the present invention;

FIG. 13 provides a bar graph of average peak to peak amplitudes duringnormal breathing with severe respiratory distress, and breathingfollowing recovery from severe respiratory distress, in accordance withembodiments of the present invention;

FIG. 14 is a flowchart showing exemplary steps of a process for treatinga patient in respiratory distress, in accordance with embodiments of thepresent invention.

SUMMARY

Systems for monitoring a patient's respiratory system may include afirst sensor communicatively coupled to a processor and configured to bepositioned on a patient's chest and capture a movement of the patient'schest, a second sensor communicatively coupled to a processor andconfigured to be positioned proximate to the patient's xiphoid processand capture a movement of the patient's xiphoid process, a third sensorcommunicatively coupled to a processor and configured to be positionedon a patient's abdomen and capture a movement of the patient's abdomen,and a power source for providing electrical power to the first, second,and third sensors. The first, second, and/or third sensors may be, forexample, accelerometers. force sensors and/or strain gauges.

In some embodiments, the system also includes a controllercommunicatively coupled to at least one of the first, second, and thirdsensors and the processor. The controller may be configured to, forexample, extract movement measurements, acceleration measurements, forcemeasurements, strain measurements, respiratory rate, and/or a degree ofthoraco-abdominal asynchrony (TAA) exhibited by the patient andcommunicate the extracted movement measurements, accelerationmeasurements, force measurements, strain measurements, respiratory rate,and/or degree of TAA to the processor.

Additionally, or alternatively, the system may include a first wiremechanically and electrically coupling the first and second sensorstogether and a second wire mechanically and electrically coupling thesecond and third sensors together. In some instances, a length of thefirst wire and/or the second wire may be adjustable via, for example, aretractable spool or when an expandable wire may be used.

In some embodiments, the processor of the system may be in communicationwith a memory with a set of instructions stored thereon, which whenexecuted by the processor cause the processor to perform a number ofsteps such as receiving a first set of sensor data from a first sensorpositioned on the epidermis of a patient in a first location, receive asecond set of sensor data from a second sensor positioned on theepidermis of the patient in a second location, determine a phasedifference between the first and second sets of sensor data and/orperform a cross-correlation analysis on the first and second sets ofsensor data, determine a degree of respiratory effort exhibited by thepatient based on a determined phase difference between the first andsecond sets of sensor data and/or a result of the cross-correlationanalysis and communicate the degree of respiratory effort to a displaydevice. In some embodiments, the processor of the system may alsoreceive a third set of sensor data from the third sensor positioned onthe epidermis of the patient in a third location, the third sensor beingin communication with the processor, determine a phase differencebetween at least one of the first and third sets of sensor data and/orthe second and third sets of sensor data and/or perform across-correlation analysis on the first and third sets of sensor dataand/or the second and third sets of sensor data, and determine a degreeof respiratory effort exhibited by the patient based on a determinedphase difference between the first and third sets of sensor data and/orthe second and third sets of sensor data and/or based on a result of thecross-correlation analysis.

Exemplary methods performed by a processor when using the inventioninclude receiving a first set of sensor data from a first sensorpositioned on the epidermis of a patient in a first location, receivinga second set of sensor data from a second sensor positioned on theepidermis of the patient in a second location, determining a phasedifference between the first and second sets of sensor data, determininga degree of respiratory effort exhibited by the patient based on adetermined phase difference between the first and second sets of sensordata, and communicating the degree of respiratory effort to a displaydevice. In some embodiments, a determination of the degree ofrespiratory effort exhibited by the patient may include determining adegree of thoraco-abdominal asynchrony (TAA) exhibited by the patient.The first location may be the patient's chest or proximate to thepatient's xiphoid process and the second location may be proximate tothe patient's xiphoid process or abdomen.

At times, the first set and/or second set(s) of sensor data may bepre-processed or filtered (e.g., bandpass filtering) prior todetermining the phase difference. The first and second sensors may be,for example, accelerometers and the first and second sets of sensor datainclude acceleration measurements. Additionally, or alternatively, thefirst and second sensors may be force meters and the first and secondsets of sensor data include force measurements. Additionally, oralternatively, the first and second sensors may be strain sensors andthe first and second sets of sensor data include strain measurements.

In some embodiments, an indication of a respiratory rate of the patientmay be received and the determination of the degree of respiratoryeffort exhibited by the patient may be further based on the respiratoryrate.

In some embodiments, a third set of sensor data may be received from athird sensor positioned on the patient in a third location. A phasedifference between first and third sets of sensor data and/or the secondand third sets of sensor data may then be determined and thedetermination of the degree of respiratory effort exhibited by thepatient may be further based on a determined phase difference betweenthe first and third sets of sensor data and/or the second and third setsof sensor data.

In some embodiments, a cross-correlation analysis between the first andsecond sets of sensor data may be completed prior to determining thedegree of respiratory effort exhibited by the patient, wherein thedegree of respiratory effort exhibited by the patient may be furtherbased on a result of the cross-correlation analysis.

In some embodiments, the first and second sets of sensor data may be asignal collected over a period of time and a result of across-correlation calculation at a particular time during the period oftime may be mapped with a maximum theoretical cross-correlation value ormaximum cross-correlation value calculated during the period of timeprior to the determination of the degree of respiratory effort exhibitedby the patient.

Additionally, or alternatively, a video recording of a patient's thoraxwhile the patient may be breathing for a period of time may be receivedso that motion of the patient's thorax, or portions thereof, may beobserved and/or measured. At times, motion may be relative movement ofthe patient's thorax while the patient may be breathing. In someembodiments, the video recording is a three-dimensional video recording.Optionally, in some cases, an epidermis of the patient's thorax may bemarked with a first marker positioned on the epidermis of the patient ina first location (e.g., chest or xiphoid process) and a second markerpositioned on the epidermis of the patient in a second location (e.g.,xiphoid process or abdomen)—but this need not always be the case.Exemplary markers include dots or graphics drawn on the skin of thepatient, stickers, LEDs, and radio-opaque markers. The video may then beanalyzed to determine changes in position of the first and secondmarkers over the period of time and a first waveform showing changes inposition of the first marker over the period of time along with a secondwaveform showing changes in position of the second marker over theperiod of time may be formed or generated. In some cases, the firstand/or second waveforms may be sinusoidal. A phase difference betweenthe first and second waveforms may be determined, and a degree ofrespiratory effort exhibited by the patient be further determined usingthe determined phase difference. Additionally, or alternatively, across-correlation analysis may be performed using the first and secondwaveforms and the degree of respiratory effort exhibited by the patientbe further determined using a result of the cross-correlation analysis.The degree of respiratory effort may then be communicated to a displaydevice as, for example, a respiratory effort score, a respiratorydistress severity score, or other indicator of respiratory effort. Insome cases, the determination of the degree of respiratory effortexhibited by the patient may include determining a degree ofthoraco-abdominal asynchrony (TAA) exhibited by the patient.Additionally, or alternatively, an indication of a respiratory rate ofthe patient, and the determination of the degree of respiratory effortexhibited by the patient may be further based on the respiratory rate.

In some embodiments, a cross-correlation analysis between the first andsecond sets of sensor data may be performed prior to determining thedegree of respiratory effort exhibited by the patient, wherein thedegree of respiratory effort exhibited by the patient may be furtherbased on a result of the cross-correlation analysis. In theseembodiments, the first and second sets of sensor data may be a signalcollected over a period of time and a result of a cross-correlationcalculation at a particular time during the period of time may be mappedwith a maximum theoretical cross-correlation value or maximumcross-correlation value calculated during the period of time prior tothe determination of the degree of respiratory effort exhibited by thepatient.

Written Description

Management of COVID-19 associated respiratory distress must consider thefull spectrum of invasive and non-invasive ventilation options becauseprolonged use of an ICU bed and ventilator consumes resources that maynot be readily available in constrained settings. Physicians must alsobalance the risk of ventilator-induced lung injury and extubationchallenges that come with prolonged ventilator use with the risk ofpoorer outcomes with inappropriately delayed intubation. The decision tointubate or offer less invasive forms of respiratory support is oftencomplicated by the degree of variability in presentation among patientswith similar levels of respiratory function. Recent guidance regardingmanagement of COVID-19 has suggested that some patients can be offerednon-invasive support such as BiPAP, CPAP or HFNC, but they must beclosely monitored for signs of respiratory effort deterioration, such assigns of increased work of breathing in the presence of hypoxia, use ofaccessory muscles, and tachypnea.

While esophageal manometry has been acknowledged as a gold standard forderiving the work of breathing from respiratory pressures, it has shownlimited clinical utility due to its invasive nature and limitedinterpretability of the output measurements. A clinical metric that hasbeen suggested as a signature of breathing effort (also referred toherein as “work of breathing”) is thoracoabdominal asynchrony (TAA), thenon-coincident motion of the rib cage and abdomen during breathing. In ahealthy patient, the chest wall and abdomen expand and retract in asynchronous manner during respiration; as the patient enters respiratorydistress, asynchronous motion of the chest and abdomen becomesincreasingly prominent. In its worst manifestation, the rib cage andabdomen move according to periodic functions that are 180° out of phase,a phenomenon referred to as “see-saw” breathing.

In addition to escalation guidance, having a feedback mechanism that canguide de-escalation of respiratory support will be critical insuccessfully and efficiently treating COVID-19 patients. Successfulextubation is especially important in COVID-19 management because of therisks of aerosolization during multiple cycles of intubation-extubation.Monitoring real-time changes in TAA could play an important role inguiding ventilatory support weaning. A recently published extubationprotocol for COVID-19 patients suggested observing for signs such as TAAduring spontaneous breathing trials (SBT) to ensure the success of SBTsduring the weaning process. Such monitoring can be especially importantfor high-risk patients in which weaning can be more challenging. Amongthese risk factors is obesity, a co-morbidity that affects up to half ofadult COVID patients. Obesity can restrict ventilation by impedingdiaphragm excursion, impairing immune responses to viral infection,promoting a pro-inflammatory state, and inducing oxidant stress that canadversely affect cardiovascular function. Importantly, TAA has beenshown to be elevated in subjects with significant abdominal obesity,raising the risk of hypoxia ventilation-perfusion mismatching andimpaired gas exchanges.

The clinical standard for TAA monitoring involves periodic visualobservation by members of the respiratory care team. Such subjectiveassessment practices can suffer from poor interobserver variability. ForCOVID-19 as well as the full spectrum of acute respiratory illness, areliable, objective assessment tool for continuous monitoring ofrespiratory effort could allow for a more complete understanding ofpatients' real-time respiratory status and provide an additionalindication or contraindication for the utilization of various levels ofventilatory support.

FIGS. 2A-2C provide graphs 210, 220, and 230, respectively, that show asinusoidal signal from a sensor positioned on a patient's chest that islabeled “C” on the graphs (sometimes referred to as a “chest signal”herein), a sinusoidal signal from a sensor positioned on the patient'sabdomen that is labeled “A” on the graphs (sometimes referred to as an“abdominal signal” herein), and a composite graph showing the first(chest) sinusoidal signal superimposed over the second (abdominal)signal so that, for example, a phase difference (Ø) therebetween may beobserved or determined. The maximum amplitude for each oscillation ofthe chest and abdominal signals is marked with an arrow. In addition,the chest and abdominal sinusoidal signals are aligned in the timedomain so that they correspond to one another in time (e.g., have thesame start and end time and progress in time at the same rate). Morespecifically, FIG. 2A provides a graph 210 that shows a first chestsignal 240A aligned in time with a first abdominal signal 245A. Graph210 also provides a composite signal 250A of the first chest signal 240Asuper first abdominal signal 245A. First chest signal 240A is highlycorrelated (i.e., high cross correlation) with first abdominal signal245A so that a phase difference (Ø) between them is approximately 0°.Because there is a high correlation between the first chest signal 240Aand the first abdominal signal 245A, the patient associated with thefirst chest signal 240A and the first abdominal signal 245A exhibitslittle, to no, TAA and is demonstrating little, or normal, effort whilebreathing.

More specifically, FIG. 2B provides a graph 220 that shows a secondchest signal 240B aligned in time with a second abdominal signal 245B.Graph 210 also provides a composite signal 250B of the second chestsignal 240B super second abdominal signal 245B. Second chest signal 240Bis not highly correlated (i.e., low cross correlation) with secondabdominal signal 245B so that a phase difference (Ø) between them isapproximately 90°. Because there is not high correlation between thesecond chest signal 240B and the second abdominal signal 245B, thepatient associated with the second chest signal 240B and the secondabdominal signal 245B exhibits some TAA, is demonstrating elevatedeffort while breathing, and is likely in some respiratory distress.

More specifically, FIG. 2C provides a graph 230 that shows a third chestsignal 240C aligned in time with a third abdominal signal 245C. Graph210 also provides a composite signal 250C of the third chest signal 240Csuper third abdominal signal 245C. Third chest signal 240C is notcorrelated (i.e., no cross correlation) with third abdominal signal 245Cso that a phase difference (Ø) between them is approximately 180°.Because there is no correlation between the third chest signal 240C andthe third abdominal signal 245C, the patient associated with the thirdchest signal 240C and the third abdominal signal 245C exhibits severeTAA and is likely exerting extreme effort while breathing and is likelyin severe respiratory distress.

In a healthy patient, the chest wall and abdomen expand and retract in asynchronous manner during respiration, with a high cross-correlation anda phase difference of approximately 0° as shown in the first compositesignal 250A of first graph 210 of FIG. 2A. As the patient entersrespiratory distress, asynchronous motion between the chest and abdomenbecomes increasingly prominent as may be seen in graph 220 of FIG. 2Band, more particularly, in the second composite signal 250B where afrequency of chest motion is 90° out of phase with the frequency ofabdominal motion. In its worst manifestation, chest and abdominalmovement become completely asynchronous, or exhibit lowcross-correlation of approximately 180° out of phase with one another asmay be seen in the third composite signal 250C of graph 230 of FIG. 2C.This phenomenon of asynchronous breathing (as shown in FIG. 2C) issometimes referred to as “see-saw” breathing.

Asynchronous breathing is a symptom of respiratory distress for alltypes of patients regardless of, for example, age, size, body massindex, waist size, chest size, and/or gender. However, in some cases, alevel, or degree, of asynchronous breathing may be dependent uponphysiological characteristics of a patient and may not be caused byrespiratory distress (e.g., a patient with a higher BMI, or largeradipose layer proximate to the abdomen may obscure extremes of movementof the abdomen or portions of the thorax and, in some cases, may notmanifest as dramatic asynchrony as an individual with a lower BMI orsmaller adipose layer). For example, in adult patients with a relativelylarge adipose tissue layer positioned on, or around, the abdomen, thisadipose tissue layer may cause some compression on the diaphragm thatmay lead to a degree of asynchronous breathing that is not resultantfrom respiratory distress. However, when such a patient is, or may be,in such respiratory distress the systems and processes described hereinmay be able to adjust measurements and other analysis to correct foradipose tissue positioned on, or around, the abdomen.

Thus, a determination of a degree of severity for asynchronous breathingof a patient may be absolute (e.g., measured against a known baseline orset of baselines) or may be relative to a patient's breathing patternwhile healthy and his or her breathing pattern while diseased state orabsolute.

FIG. 3A presents an exemplary system 300 that may be configured toexecute one or more methods disclosed herein. In some cases, system 300(or portions thereof) may gather data that may be used to assessrespiratory effort of a patient and make determinations of respiratorydistress (e.g., a respiratory distress score) for the patient usingsystem 300, or portions thereof. System 300 includes a sensor array 310configured to measure chest and abdomen motion during respiration, acontroller 320 configured to received data from the sensor array 310,extract, for example, movement, TAA, and/or respiratory rate from thesensor array data and provide the extracted data to a computer system330 that, in many cases, includes a display interface to visualize datafor viewing by a user. In some embodiments, controller 320 may be amicrocontroller. System 300 may also include a power source 360 that maybe electrically coupled to one or more components of system 300. Powersource 360 may be configured to provide electrical power to one or morecomponents of system 300. Exemplary power sources include but are notlimited to a battery and a mechanism by which to plug into a wall outletand draw power from a main power supply.

Sensor array 310 may include a plurality (e.g., 2-10) sensors that maybe configured to sense movement of a patient. Exemplary sensors includedin sensor array 310 include, but are not limited to, accelerometers(e.g., 2-dimensional and/or three-dimensional accelerometers), forcemeters, and/or strain-based sensors (sometimes referred to as straingauges). Exemplary accelerometers that may be included in sensor array310 are an Invensense ICM-20602 6-axis gyroscope and/or accelerometerwith acceleration sensitivity of ±2 g, ±4 g, ±8 g, or ±16 g. Exemplarystrain-based sensors include a piezo-resistive metal thin film set in asubstrate such as a silicone or rubber elastomer substrate.

Controller 320 may be configured to sample data from sensor array 310 atany preferred rate (e.g., 4 kHz or below) that allows for small (e.g.,0.1-5 mm) patient movements to be measured. In some embodiments, sensordata may be collected from the sensor array 310 following the I²Ccommunication protocol using controller 320, which may receive theaccelerator data from each accelerometer at, for example, an exemplaryfrequency of 30.5 Hz. Controller 320 may then communicate the sampledaccelerometer data to a PC for processing according to, for example, oneor more processes described herein. Components of system 300 maycommunicate via wired and/or wireless means and, in some embodiments,may communicate using a communication network like the Internet.

In some embodiments, the sensors of sensor array 310 and/or controller320 may be physically/electrically coupled to one another and/or othercomponents of system 300. Additionally, or alternatively, one or more ofthe sensors of sensor array 310 and/or controller 320 may be wirelesslycoupled to one another and/or other components of system 300 via, forexample, a wireless or near-field communication protocol (e.g.,BLUETOOTH™). When the sensors of sensor array 310 and/or controller wire320 are configured for wireless communication they may include awireless antenna and/or transceiver (not shown).

System 300 may also include a database 340 configured to store datareceived by computer system 330, a display device 350 communicativelycoupled to computer system 330, and a camera 360, which may be a videocamera configured to capture video images of a patient while he or shebreathes. In one embodiment, a camera 360 is a high speed cameraconfigured to capture, for example, 1,500-3,000 frames per minute. Twoor more components of system 300 may be communicatively coupled to oneanother via, for example, a network 305 such as the Internet.

FIG. 3B is a block diagram showing an exemplary computer system 370 thatincludes a bus 372 or other communication mechanism for communicatinginformation, and a processor 374 coupled with the bus 372 for processinginformation. Computer system 370 also includes a main memory 376, suchas a random-access memory (RAM) or other dynamic storage device, coupledto the bus 372 for storing information and instructions to be executedby processor 374. Main memory 376 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 374. Computer system 370further includes a read only memory (ROM) 378 or other static storagedevice coupled to the bus 372 for storing static information andinstructions for the processor 374. A storage device 380, for example ahard disk, flash memory-based storage medium, or other storage mediumfrom which processor 374 can read, is provided and coupled to the bus372 for storing information and instructions (e.g., operating systems,applications programs and the like).

Computer system 370 may be coupled via the bus 372 to a display 382,such as a flat panel display, for displaying information to a computeruser. An input device 384, such as a keyboard including alphanumeric andother keys, may be coupled to the bus 372 for communicating informationand command selections to the processor 374. Another type of user inputdevice is cursor control device 386, such as a mouse, a track pad, orsimilar input device for communicating direction information and commandselections to processor 374 and for controlling cursor movement on thedisplay 382. Other user interface devices, such as microphones,speakers, etc. are not shown in detail but may be involved with thereceipt of user input and/or presentation of output.

The processes referred to herein may be implemented by processor 374executing appropriate sequences of computer-readable instructionscontained in main memory 376. Such instructions may be read into mainmemory 376 from another computer-readable medium, such as storage device380, and execution of the sequences of instructions contained in themain memory 376 causes the processor 374 to perform the associatedactions. In alternative embodiments, hard-wired circuitry orfirmware-controlled processing units may be used in place of or incombination with processor 374 and its associated computer softwareinstructions to implement the invention. The computer-readableinstructions may be rendered in any computer language.

In general, all of the above process descriptions are meant to encompassany series of logical steps performed in a sequence to accomplish agiven purpose, which is the hallmark of any computer-executableapplication. Unless specifically stated otherwise, it should beappreciated that throughout the description of the present invention,use of terms such as “processing”, “computing”, “calculating”,“determining”, “displaying”, “receiving”, “transmitting” or the like,refer to the action and processes of an appropriately programmedcomputer system, such as computer system 370 or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within its registers and memories intoother data similarly represented as physical quantities within itsmemories or registers or other such information storage, transmission ordisplay devices.

Computer system 370 also includes a communication interface 388 coupledto the bus 372. Communication interface 388 may provide a two-way datacommunication channel with a computer network, which providesconnectivity to and among the various computer systems discussed above.For example, communication interface 388 may be a local area network(LAN) card to provide a data communication connection to a compatibleLAN, which itself is communicatively coupled to the Internet through oneor more Internet service provider networks. The precise details of suchcommunication paths are not critical to the present invention. What isimportant is that computer system 370 can send and receive messages anddata through the communication interface 388 and in that way communicatewith hosts accessible via the Internet. It is noted that the componentsof system 370 may be located in a single device or located in aplurality of physically and/or geographically distributed devices.

FIG. 4A is an illustration of an exemplary sensor array 310 thatincludes three sensor modules 420: a first sensor module 420A, which maybe configured to be positioned on a patient's chest (and may besometimes referred to herein as a “chest sensor”), a second sensormodule 420B, which may be configured to be positioned on a patient'sxiphoid process (and may be sometimes referred to herein as a “xiphoidsensor”), and a third sensor module 420C, which may be configured to bepositioned on a patient's abdomen (and may be sometimes referred toherein as a “abdomen sensor”) as shown in, for example, FIG. 4C. First,second, and third sensors 420A, 420B, and 420C may be, for example,accelerometers, strain gauges, and/or force meters that are physicallyand electrically coupled (in series and/or parallel) to one another viaby a plurality (e.g., 4, 8, 10) of wires that may be included in asingle, or multiple, cable(s) 440. The individual wires/cables 440 maybe soldered to leads provided by first, second, and third sensors 420A,420B, and 420C. Although shown as wired, first, second, and thirdsensors 420A, 420B, and 420C may, in some cases, be configured totransmit the signals wirelessly or in differing numbers of wiringconfigurations. Sensor array 310 may be coupled to controller 320 viawire 440. In some cases, wire 440 may be long enough to accommodateplacement of the controller a preferred distance (e.g., 10 or 15 feet)away from the patient on whom the sensor array 310 is placed. In someembodiments, wires/cables 440 may be sized to fit different body sizes(e.g., infant, pediatric, adolescent, and adult). Additionally, oralternatively, wires/cables 440 may be of an adjustable length via, forexample, spool or retraction mechanism present in a housing for one ormore of sensors 420 that facilitate the extension and/or retraction ofwires/cables 440 to fit different body sizes. In some embodiments, thewires/cables 440 may be flexible and/or an attachment mechanism betweenthe wires/cables 440 and the sensor is flexible.

FIG. 4B is an illustration of another exemplary sensor array 310 thatincludes three sensor modules 420 similar to those shown in FIG. 4A.Along with the components of sensor array shown in FIG. 4A, the sensorarray 310 of FIG. 4B includes a wire expansion mechanism 455 that may beconfigured to make a length of a cable/wire 440 adjustable via, forexample, retraction and/or expansion by way of a spool or elasticmechanism.

FIG. 4C provides an exploded view of sensor array 310 where each sensor420 includes a set of sensor circuitry and/or mechanics 425 configuredto, for example, sense movement or acceleration, a detachable adhesivepatch 450 configured to adhere to the skin of a patient, a clip 460 thatmay attach to, for example, an electro cardio gram (ECG) pad, and a case470 that houses clip 460 and sensor circuitry and/or mechanics 425.Sensor circuitry and/or mechanics 425 may be, for example, a printedcircuit board that, in some cases includes a MEMS IMU and supportinghardware, a force sensor device, a stress gauge, and/or anaccelerometer. Sensor array 310 of FIG. 4D also shows lengths of wire440.

The spacing of sensors 420 and/or sensor circuitry and/or mechanics 425may be configured to align with anatomical measurements of the distancebetween the chest and xiphoid process and between the xiphoid and apexof the abdomen of a patient and may be of differing lengths toaccommodate differing ages and body types, such as a children 1 to 5years of age, an adolescent 13-15 years of age, or an adult (18-90 yearsof age). In some cases, a length of one or more wires 440 may beadjustable in order to accommodate, for example, different bodytypes/sizes. For example, a housing for one or more sensors 420 mayinclude a mechanism (e.g., spool) that may enable one or more wires 440to retract into the housing. Additionally, or alternatively, a componentof a sensor array may be elastic or otherwise configured to expand orcontract so that positioning between sensors 420 may accommodate thephysiology of an individual. In some embodiments, a first accelerometer420 may be configured to be placed on the wearer's chest (e.g., amidpoint between the patient's nipples), a second accelerometer 420 maybe configured to be placed on the patient's xiphoid process, and a thirdaccelerometer 420 may be configured to be placed on the patient'sabdomen.

FIG. 4D is an illustration of a patient 480 with a sensor array 310positioned thereon. In some cases, sensor array 310 may be positionedonto patient 480 when the user (e.g., heath care provider) adheres thefirst sensor 420A at the midpoint between the nipples, adheres thesecond sensor 420B on the epidermis proximate to the patient's xiphoidprocess, and adheres the third sensor 420C onto the abdomenapproximately 1-3 inches above the navel for a pediatric patient, or 2-6inches above the navel for an adult patient.

FIG. 5A is a flowchart showing exemplary steps of a process 500 fordetermining a patient's respiratory rate. Process 500 may be executedby, for example, system 300 or any component thereof.

Initially, in step 505, sensor data may be received in the form of, forexample, a waveform 530 with a plurality of peaks 540 as shown in FIG.5B. Oftentimes, the sensor data received is data from an abdominalsensor like abdominal sensor 420C. The sensor data may be time stampedand/or divided into a plurality of time windows examples of which areshown in FIG. 5B as first time window 535 a and second time window 535b. In some embodiments, the sensor data may be filtered using, forexample, a bandwidth filter.

The received sensor data may then be analyzed using, for example, a peakdetection function to detect peaks in the sensor data (step 510). Thesepeaks may correspond to a maximal expansion of the abdominal cavity,which occurs once per respiratory cycle and thus are correlated with therespiratory cycle of the patient. In some cases, peaks in the data maybe characterized by a threshold separation by a number of points and athreshold prominence relative to surrounding local maxima, wherebythreshold separation means that each peak is separated by a certainnumber of points. For example, if a peak is identified at point x andthreshold separation is defined as 10 points, that means the earliestanother peak can be identified is at point x+10. This prevents peaksfrom being sampled from the data too frequently. Separation may beequivalent to a distance input for the python function disclosed herein.Threshold prominence may provide an indicator of relative amplitude.Noise within a signal has some typical amplitude, and the signal contentof interest (e.g., amplitude or number of breaths in a sample) may havea common, or typical, amplitude. Setting a prominence threshold allowsyou to set how “prominent” a peak has to be relative to other possiblepeaks to be actually marked as a peak.

In step 515, a duration of time separating each pair of consecutivepeaks may be determined for a plurality of peaks and/or time windows.Then, an average value for the time separating the peaks may bedetermined (step 520) and this average time value may be converted intoa respiratory rate (step 525) wherein, for example, an average number ofpeaks within a given time window (e.g., 1 minute) corresponds to anumber of breaths per minute (i.e., respiratory rate).

FIG. 6 is a flowchart showing exemplary steps of a process 600 fordetermining a patient's TAA, a degree of respiratory distress exhibitedby the patient, and/or a respiratory distress score for the patient.These determinations may be performed on, for example, a periodic,as-needed, and/or continuous basis. Process 600 may be executed by, forexample, system 300 or any component thereof such as sensor array 310.

Initially, a first and second set of sensor data may be received by aprocessor or computer like computer system 330 (step 605). In someembodiments, the first and second sets of sensor data are waveforms likethose shown in FIGS. 2A-2C. The sensor data may be received from, forexample, a controller like controller 320 and/or a sensor like firstsensor 420A, second sensor 420B, and/or third sensor 420C. The sensordata may correspond to, for example, acceleration data, a forcemeasurement, a strain measurement, and/or a measured change in diameterof, for example the thorax, chest, xiphoid process area, and/or abdomenof a patient and may be taken over time (e.g., 30 s, 1 minute, 5minutes, etc.). At times, data corresponding to multiple measurementsmay be received in step 605. For example, data corresponding to ameasurement taken at the patient's chest from, for example, first sensor420A, data corresponding to a measurement taken at the patient's xiphoidprocess from, for example, second sensor 420B, and/or data correspondingto a measurement taken at the patient's abdomen from, for example, thirdsensor 420C may be received in step 605. In some embodiments, differenttypes of data corresponding to a measurement taken from a particularlocation (e.g., chest, xiphoid process, and/or abdomen) may be receivedin step 605. For example, data corresponding to acceleration, force,and/strain measurements for one or more of the particular locations onthe patient's chest may be received so that, for example, multiple typesof measurements may be used to validate and/or establish a confidencelevel for an accuracy of determinations using the received data.

The received sensor data may then be filtered, analyzed, and/orpre-processed (step 610). In some cases, the analysis and pre-processingof step 610 may include, for example, filtering the data and/orperforming a phase shift analysis using, for example, a Hilberttransform filter so that a phase angle between the resulting functionsmay be determined (step 615).

The Hilbert transform filter is a mathematical function that can be usedto convert real signals into analytic signals, defined as signals withno-negative frequency components. A continuous time analytic signal canbe represented as Equation 1, below:

$\begin{matrix}{{z(t)} = {\frac{1}{2\pi}{\int_{0}^{\infty}{{Z(\omega)}e^{j\omega t}d\omega}}}} & {{Equation}1}\end{matrix}$Where:

-   -   z(t)=Analytic representation    -   t=time    -   Z(ω)=the complex coefficient of the positive-frequency signal        and sets its amplitude and phase;    -   ω=frequency    -   dω=the derivative of the frequency        Real sinusoids can be converted to positive frequency complex        sinusoids by generating a phase quadrature component to serve as        the imaginary part; this phase-quadrature component is generated        by shifting the original signal by 90°. The Hilbert transform        filter has the effect of filtering out negative frequencies and        creating a gain of 2 for positive frequencies.

The Hilbert transform can be explained mathematically wherein if twosignals are perfectly synchronous, the resulting phase angle approaches0° while during paradoxical motion phase angle approaches 180°.

The Hilbert transform can be explained mathematically by the followingcalculations of Equations 2A and 2B where x(t) is a sinusoidal signalwith unit amplitude, frequency ω₀, positive frequency components X₊ andnegative frequency components X⁻ where:x ₊(t)

e ^(jω) ⁰ ^(t)  Equation 2Ax ⁻(t)

e ^(−jω) ⁰ ^(t)  Equation 2BApplication of a −90° phase shift

$\left( e^{- \frac{j\pi}{2}} \right)$to the positive frequency component (X₊) and a +90° phase shift

$\left( e^{\frac{j\pi}{2}} \right)$to the negative frequency component (X⁻) is represented by Equations 3Aand 3B, respectively.y ₊(t)=−je ^(jω) ⁰ ^(t)  Equation 3Ay ⁻(t)=je ^(−jω) ⁰ ^(t)  Equation 3A

-   -   Then, adding the original and shifter components together as a        single signal (x(t)+jy(t)) yields Equations 4A and 4B,        reproduced below.        z ₊(t)=e ^(jω) ⁰ ^(t) −j ² e ^(jω) ⁰ ^(t)=2e ^(jω) ⁰ ^(t)=2x        ₊(t)  Equation 4A        z ⁻(t)=je ^(−jω) ⁰ ^(t) +j ² e ^(−jω) ⁰ ^(t)=0  Equation 4B

In processing of discrete time signals using software such as MATLABand/or the Python script library, the Hilbert transform is computed byfirst calculating the Fourier transform of the signal. The amplitude ofthe negative frequency components of the signal is then set to zero.Finally, a new signal is generated by calculating the inverse Fouriertransform of the new frequency space.

Using the Hilbert transform may allow for signals that are approximatelysinusoidal, such as respiratory signals, to be defined with a singlecharacteristic frequency. In some embodiments, determining thecharacteristic frequency of the data from two or more positions/sensorson the patient's body (e.g., xiphoid and navel positions) may allow forthe determination of a phase shift between the signals. This may be doneafter identifying the window of recently collected data over which phaseshift may be calculated and normalizing the data by subtracting away themean of the data points contained within the window and dividing by thestandard deviation. Once normalized, the data may be sent through aHilbert transform filter and phase shift may be calculated. FIG. 8provides a graph 800 wherein a phase shift Ø between the Hilberttransform-filtered amplitude of data received from a sensor placedproximate to the patient's navel or abdomen (sometimes referred toherein as the third signal) and referred to in FIG. 8 as a navel, signal(N) and data received from a sensor placed proximate to the patient'sxiphoid process (sometimes referred to herein as the second signal) andreferred to in FIG. 8 as a xiphoid, signal (X) are plotted in thecomplex plane where the Y-axis corresponds to imaginary numbers (labeledIm on graph 800) and the X-axis corresponds to real numbers (labeled Reon graph 800). The navel signal (N) may be expressed as Equation 5,below:Navel Signal=Ne ^(−i(wt+Ø))  Equation 5

Where:

-   -   N=amplitude shift of the navel signal    -   e=Euler's number (approximately 2.71828)    -   ω=frequency    -   Ø=phase shift    -   t=time        The xiphoid signal may be expressed as Equation 6, below:        Xiphoid Signal=Xe ^(−iwt)  Equation 6

Where:

-   -   X=amplitude shift of the xiphoid signal    -   e=Euler's number (approximately 2.71828)    -   ω=frequency    -   t=time

Optionally, in step 620, a cross-correlation analysis of the two sets ofdata received in step 605 (e.g., data from the second and third sensors)may be performed in addition and/or alternatively to the phasedifference determination of step 615. Results of the cross-correlationanalysis (also referred to herein as cross-correlation data) for amoment in particular time may be mapped to the maximum cross correlationcalculated over the course of data collection (step 625). The course ofdata collection may occur for a time period lasting, for example, 15seconds, 30 seconds, 60 seconds, 5 minutes, 10 minutes and/or an hour.In some cases, the collection of data may be continuous and/or periodicover a longer period of time (e.g., 4, 12, 24, 48, or 82 hours). In someembodiments, the cross-correlation analysis may be based upon timeintegration of the two or more signals. For example, thecross-correlation determined at a given time may be mapped from 0 to100% relative to the maximum cross correlation calculated over thecourse of the data collection; this output, or mapping, may be referredto as the “relative cross correlation”. The phase shift analysis of step615 and/or the cross correlation analysis of step 620 may be performedover time periods in a manner similar to how the respiratory ratecalculation is performed via process 500.

In some embodiments, the cross-correlation of two discrete functionsf[n] and g[n], or data sets, may be defined as shown in Equation 8,below:

$\begin{matrix}{{\left( {f\bigstar g} \right)\lbrack n\rbrack} \equiv {\sum\limits_{m = {- \infty}}^{\infty}{{f^{*}\lbrack m\rbrack}{g\left\lbrack {m + n} \right\rbrack}}}} & {{Equation}8}\end{matrix}$

-   -   Where:    -   f=a signal corresponding to a first data set    -   g=a signal corresponding to a second data set    -   n=a lag between functions    -   m=maximum value of a signal corresponding to either the first or        second data set over a period of time

For two noise distorted, approximately periodic discrete signals withequal periods, the cross-correlation function of two signals with lag nranging from the negative to the positive sum of the number of points ineach signal may look approximately as shown in FIG. 9 , where themaximum will occur at zero lag time and local maxima occur at shiftsequal to the period. FIG. 9 provides a graph 900 that shows a motioncapture test where lag time in seconds is shown as a function of crosscorrelation for xiphoid and navel respiratory signals determined duringvarious stages of respiration distress wherein curve 910 showscross-correlation as a function of lag time for normal breathing, curve920 shows cross-correlation as a function of lag time for breathing whenin severe respiratory distress, and curve 930 shows cross-correlation asa function of lag time for breathing when in recovery from severerespiratory distress. FIG. 9 shows that maximal correlation occursbetween the two signals under normal conditions; correlation decreasesin severe respiratory distress; and correlation returns to near baselineupon recovery from respiratory distress.

Optionally, in step 630, an indication of a respiratory rate variabilityof the patient may be received. This indication may be determined usingthe first and second sets of sensor data and/or may be input fromanother device and/or attending care giver.

Optionally, in step 635, additional information about the patient may bereceived and/or determined. Exemplary received additional informationincludes information pertaining to a physiological characteristic of thepatient such as body mass index (BMI), a thickness of adipose tissue onthe patient's abdomen, a weight of the patient, a size of the patient,the patient' respiratory rate (e.g., breaths per minute), mental status,blood oxygen saturation, and/or whether the patient is on supplementaloxygen or other respiratory assistance. Exemplary determined additionalinformation includes respiratory rate (e.g., breaths per minute) whichmay be determined using, for example, a process like process 500described above with regard to FIG. 5A, as well as respiratory rate s, ameasure of the variation in length of time of each respiratory cycle.

In step 640, the phase shift analysis data, mapped cross-correlationdata and/or the additional information received in step 635 may be usedto determine a degree of respiratory effort exhibited by the patient,which may be used to determine a level of respiratory distress (i.e., arespiratory distress score) for the patient (step 645), where decreasedcross-correlation and increased phase shift between two or morecollected signals indicate increased thoraco-abdominal asynchrony (TAA).In some cases, the degree of respiratory effort exhibited by the patientmay be, and/or may include a degree of TAA exhibited by the patient. Thedegree of respiratory effort and/or respiratory distress may then beprovided to a display device such as a computer monitor or other displaydevice (step 650).

In some embodiments, not all of the steps of process 600 are performedto determine a degree of respiratory distress (step 640) and/ordetermine a respiratory distress severity score (step 645). For example,in some embodiments, the determinations of steps 640 and/or 645 areperformed using only the phase difference of step 615, a result of thecross-correlation analysis of step 620, a mapping of thecross-correlation data of step 625, a determination of an indication ofrespiratory variability of step 625. Alternatively, a result ofexecution of two or more steps of process 600 may be used to determine adegree of respiratory distress (step 640) and/or determine a respiratorydistress severity score (step 645). For example, a combination ofresults from execution of steps 615 and 620, combination of results fromexecution of steps 615, 620, and 625, combination of results fromexecution of steps 615, 620, 625, and 630, combination of results fromexecution of steps 620, 625, and/or 630, and/or a combination of resultsfrom execution of steps 625 and 630 may be combined to determine adegree of respiratory distress (step 640) and/or determine a respiratorydistress severity score (step 645).

FIG. 7 is a flowchart showing exemplary steps of another process 700 fordetermining a patient's TAA, a degree of respiratory distress exhibitedby the patient, and/or a respiratory distress score for the patient.These determinations may be performed on, for example, a periodic,as-needed, and/or continuous basis. Process 700 may be executed by, forexample, system 300 or any component thereof such as sensor array 310.

Initially, in step 705, a first set of cross-correlation data, a firstdegree of respiratory effort exhibited by the patient, and/or a firstrespiratory distress severity score for the patient may be received via,for example, execution of process 600 or a portion thereof. In someembodiments, the information received in step 705 may be a baseline setof cross-correlation data, a baseline respiratory effort exhibited bythe patient, and/or a baseline respiratory distress severity score that,in some cases may be previously determined as part of, for example, aroutine medical exam. These baselines may assist with the establishmentof how much effort a patient exhibits while breathing under normalconditions for the patient (e.g., not when acutely ill). Using baselinesin this way may allow determinations of respiratory effort to factor inindividual differences when determining whether or not the patient is inrespiratory distress and/or quantifying a degree of respiratory distressor determining a respiratory distress score for the patient. This may behelpful when, for example, the patient exhibits impaired breathing undernormal conditions as may be the case with a chronic respiratorydiagnosis (e.g., asthma, chronic pulmonary obstructive disease (COPD),or lung cancer). Additionally, or alternatively, the informationreceived in step 705 may be a set of cross-correlation data, a degree ofrespiratory effort exhibited by the patient, and/or a respiratorydistress severity score determined for the patient prior (e.g., minutes,hours, days) to execution of process 700.

In step 710, a third and a fourth set of sensor data may be received bya processor or computer like computer system 330. In some embodiments,the third and fourth sets of data are from different sensors positionedon different portions of the patient's body (e.g., chest and abdomen orxiphoid process and abdomen). In some embodiments, the third and fourthsets of sensor data are waveforms like those shown in FIGS. 2A-2C and,at times, the third and fourth sets of sensor data may be similar to thefirst and second sets of sensor data received in step 605.

The received sensor data may then be filtered, analyzed, and/orpre-processed (step 715). Execution of step 715 may be similar toexecution of step 610 described above with the exception that thefiltering, analysis, and/or pre-processing is performed on the third andfourth sets of sensor data. Then, a phase difference between the thirdand fourth sensor data sets may be determined (step 720). Execution ofstep 720 may be similar to execution of step 615.

A cross-correlation analysis of the third and fourth sets of data maythen be performed (step 725) and the results of this cross-correlationanalysis (also referred to herein as cross-correlation data) for amoment in particular time may be mapped to the maximum cross correlationcalculated over the course of data collection (step 730). In someembodiments, execution of steps 725 and 730 may be performed in a mannersimilar to execution of steps 620 and 625, respectively.

Optionally, in step 735, additional information about the patient may bereceived and/or determined. The additional information received in step735 may be similar to the additional information received in step 635.

In step 740, the mapped cross-correlation data for the third and fourthdata sets and/or the additional information received in step 735 may beused to determine a second, or subsequent, degree of respiratory effortexhibited by the patient, which may be used to determine a second, orsubsequent, level of respiratory distress (i.e., a respiratory distressscore) for the patient (step 745). In some cases, the degree ofrespiratory effort exhibited by the patient may be, and/or may include adegree of thoraco-abdominal asynchrony (TAA) exhibited by the patient.

In step 750, the mapped cross-correlation data for the third and fourthdata sets may be compared with the mapped cross-correlation data for thethird and fourth data sets in order to determine a differencetherebetween. This difference may be used to adjust or qualify (e.g.,elevated or improving) the second determined degree of respiratorydistress determined in step 740 and/or the second respiratory scoredetermined in step 745. Additionally, or alternatively, step 750 may beperformed prior to step(s) 740 and/or 745 and the comparison may be usedto determine the second degree of respiratory distress determined instep 740 and/or the second respiratory score determined in step 745.

Additionally, or alternatively, step 750 may include a comparison of thedegree of respiratory effort received in step 705 with the second degreeof respiratory effort determined in step 740. This difference may beused to adjust or qualify (e.g., elevated or improving) the seconddegree of respiratory distress determined in step 740 and/or the secondrespiratory score determined in step 745. Additionally, oralternatively, step 750 may be performed prior to step(s) 740 and thecomparison may be used to determine the second degree of respiratorydistress determined in step 740 and/or the second respiratory scoredetermined in step 745.

Additionally, or alternatively, step 750 may include a comparison of thedegree of a respiratory distress score received in step 705 with thesecond respiratory distress score determined in step 745. Thisdifference may be used to adjust or qualify (e.g., elevated orimproving) the second degree of respiratory distress determined in step740 and/or the second respiratory score determined in step 745.Additionally, or alternatively, step 750 may be performed prior tostep(s) 745 and the comparison may be used to determine the seconddegree of respiratory distress determined in step 740 and/or the secondrespiratory distress score determined in step 745.

Optionally, in step 755, the second degree of respiratory distressdetermined in step 740 and/or the second respiratory distress scoredetermined in step 745 may be updated and/or recalculated using thecomparison results of step 750.

In step 760, the second degree of respiratory effort and/or secondrespiratory distress severity score and/or the updated and/orrecalculated second degree of respiratory effort and/or secondrespiratory distress severity score for the patient may be communicatedto a display device.

FIG. 10 is a flowchart showing exemplary steps of a process 1000 forgathering information regarding movements of a patient's thorax andportions thereof while breathing over time and assessing whether thepatient is in respiratory distress using, for example, system 300 and/orcomponents thereof.

In step 1005, an image of a patient with a plurality of markerspositioned thereon may be received. The markers may mark, or delineate,different positions on the thorax of the patient. The position of themarkers in the image received in step 1005 may represent an originalposition, or origin, for the marker against which motion up and down,left and right may be measured.

FIG. 11 provides an example of an image 1100 that may be received instep 1005 where different markers, in the form of dots drawn on thepatient and tabs sticking up from the patient delineate differentpositions on the patient's thorax, or chest. More specifically, FIG. 11shows a first marker 1105 positioned on an upper region of the patient'schest, a second marker 1110 positioned below the sternum, a third marker1115 positioned at, or proximate to, the navel, a fourth marker 1120positioned in approximately at a first intercostal space (e.g., betweenthe fifth and sixth rib), a fifth marker 1125 positioned inapproximately at a second intercostal space (e.g., between the sixth andseventh rib), a sixth marker 1130 positioned in approximately at a thirdintercostal space (e.g., between the seventh and eighth rib), a seventhmarker 1135 positioned in approximately at a fourth intercostal space(e.g., between the eighth and ninth rib). In some embodiments, first,second, and/or third marker 1105, 1110, and/or 1115 may include a first,second, and/or third reference mark 1106, 1117, and/or 1116,respectively, which may be configured to assist with the video captureof movement by the patient while breathing in, for example, the X, Y,and/or Z-direction(s). In some cases, the reference point may be in theform of a crosshair or “+” sign to, for example, aid with analysis of avideo recording of the patient to determine movement of the patientwhile he or she breathes. Also shown in FIG. 11 are optionalsub-markers. Motion of the patient's chest and abdomen may be observedand quantified via the first-seventh markers 1105-1135. For example, avideo camera, such as video camera 360, may record movements of thepatient's chest while breathing and this video may be received in step1010. The video may be analyzed to, for example, determine movement ofthe markers over time (step 1015). In some embodiments, a plurality ofvideos may be received in step 1010 and may be analyzed/quantified viathe first-eighth markers under different breathing conditions (e.g.,unrestricted and restricted) for the patient. For example, a videorecording of patient breathing may be taken when breathing isunrestricted (e.g., normal); when there is resistance applied to thepatient's chest and/or breathing via, for example, an elastic bandand/or an exercise mask with fixed resistance with no time to acclimateto breathing with resistance; and/or when there is resistance applied tothe patient's chest and/or breathing via, for example, an elastic bandand/or an exercise mask with fixed resistance with an interval of time(e.g., 3-8 minutes) for the patient to acclimate to breathing withresistance. These recordings may then be analyzed to determine how muchfirst-seventh markers 1105-1135 move over time under the differingconditions for the patient.

FIG. 12 provides an exemplary graph 1200 showing three Lissajous curvesthat plot abdominal movement in inches as a function of rib cagemovement measured in inches where a first Lissajous curve 1210represents abdominal movement as a function of rib cage movement at whenthe patient is in recovery from respiratory distress, a second Lissajouscurve 1220 represents abdominal movement as a function of rib cagemovement when the patient is breathing with severe respiratory distress,and a third Lissajous curve 1230 represents abdominal movement as afunction of rib cage movement when the patient experiences normalbreathing. The first, second, and third Lissajous curves 1210, 1220, and1230 reflect variation in amplitude of movement for each of the threetypes of breathing (i.e., recovery from respiratory distress, severerespiratory distress, and normal breathing, respectively) wherein, forthis example, there is a wider range in amplitude for breathing whenrecovery from respiratory distress as compared to normal breathing(i.e., second and third Lissajous curves 1220 and 1230) and a recovery(i.e., relatively smaller changes in amplitude for abdominal movementcompared with rib cage movement) shown by first Lissajous curve 1210demonstrated by the recovery from respiratory distress breathing. Thisshows how a comparison for amplitudes of a patient's abdominal and rigcage movement may assist in quantifiably characterizing the extent towhich a patient is experiencing respiratory distress.

Optionally, in step 1020, a cross-correlation of the data from two ormore of the markers may then be performed in, for example, a mannersimilar to the cross-correlation analysis of step 620.

Optionally, in step 1025, a variation in amplitude for one or more ofthe markers over time may be determined. As an example, FIG. 13 providesa bar graph of average peak to peak amplitudes during normal breathing(bar graph with no fill (or white)), breathing with severe respiratorydistress (shown with bar graphs with dashed horizontal fill lines), andbreathing following recovery from severe respiratory distress (shownwith bar graphs with diagonal fill lines) for the first-seventh markers1105-1135. Graph 1300 also provides an indication of a range of errorfor each type of breathing in the form of an error bar. In thisinstance, the error bar represents a 95% confidence interval.

In step 1030, it may be determined whether the patient is in respiratorydistress (i.e., has breathing similar to the restricted breathing) andan indication of whether the patient is in respiratory distress may beprovided to a user such as a clinician, doctor, or nurse (step 1035).

A recently proposed treatment algorithm for patients with hypoxia due toCOVID-19 suggests monitoring for signs including TAA when consideringescalation of respiratory support from HFNC therapies to mechanicalventilation. This is because some patients whose respiratory rate andthoracoabdominal asynchrony are not rapidly relieved with HFNC arepotentially at high risk of HFNC failure. Multiple studies suggest thatwhile HFNC and non-invasive ventilation (NIV) may be sufficient for themanagement of respiratory failure in COVID-19 when utilized earlyenough, but the data are far from conclusive—stronger, evidence-basedindications for selecting among forms NIV and selecting between NIV andinvasive ventilation are needed.

FIG. 14 is a flowchart showing exemplary steps of a process 1400 fortreating a patient in respiratory distress using, for example, system300 and/or components thereof, such as sensor array 310.

In step 1405, a set of sensor data for a patient may be received. Thesensor data may be similar to the sensor data received in step 605 asexplained above with regard to FIG. 6 . In some embodiments, the firstand second sets of data are from different sensors positioned ondifferent portions of the patient's body (e.g., chest and abdomen orxiphoid process and abdomen). In some embodiments, the sensor data maybe received when the patient arrives at a treatment facility (e.g.,hospital for urgent care center) and/or when the patient is monitored athome for respiratory distress. Prior to step 1405, a sensor array, suchas sensor array 310 may be placed on the patient's chest, xiphoidprocess, and abdomen so that data regarding how the chest, xiphoidprocess, and abdomen are moving when the patient is breathing. In step1410, a determination of whether the patient is experiencing respiratorydistress may be made by, for example, executing process 600, or portionsthereof. An indication of the determination of step 1410 may then beprovided to a clinician or caregiver for the patient. For the purposesof discussion of process 1400, the range of respiratory distressdeterminations are none, minor, moderate, or severe distress but it willbe understood by those in the art that the indication of respiratorydistress may be made and provided to a clinician in any appropriateformation (e.g., a numerical score or a graphic).

When the patient is not experiencing respiratory distress, he or she maybe discharged from the treatment facility (step 1485). When it isdetermined that the patient is experiencing minor respiratory distress,a treatment such as albuterol may be administered (step 1415) and thepatient may continue to be monitored to determine if the treatment iseffective. In step 1420, another set of sensor data may be received, andit may be determined whether the patient is still in respiratorydistress following treatment (step 1425). If the patient is no longer inrespiratory distress, or if the respiratory distress is consideredmanageable in an out-patient setting as may be the case with a patientwho is in recovery from a respiratory disease and/or a chronically-illpatient with, for example, chronic obstructive pulmonary disease (COPD),he or she may be discharged from the treatment facility (step 1485).When the patient is still in respiratory distress, he or she may beadmitted to the treatment facility (e.g., hospital) for furthertreatment of his or her respiratory distress (step 1430).

When it is determined in step 1410, that the patient's respiratorydistress is severe, the patient may be admitted to the treatmentfacility (e.g., hospital) for further treatment of his or herrespiratory distress (step 1430). Upon admission to the treatmentfacility via the determination of 1410 or 1425, additional sensor datamay be received (step 1435) so that a level of respiratory distress maybe determined (step 1440) and a determination of whether to place thepatient in the intensive care unit (severe respiratory distress) or onthe floor of the hospital (moderate) may be made using the respiratorydistress determination of step 1440. In some embodiments, step 1435 and1440 may not be performed and the determination of whether to place thepatient in the intensive care unit or on the floor of the hospital maybe made using the respiratory distress determinations of steps 1410 orstep 1425.

In step 1445, the patient may be placed in an intensive care unit (ICU)for further treatment (step 1450) with, for example, albuterol, HFNC,NIPPV, IPPV, and/or sedation and ventilation depending on the severityof respiratory distress and the patient's responsiveness to treatment.In order to determine the patient's responsiveness to treatment, thepatient may be monitored, and an additional set of sensor data may bereceived (step 1455) on a continuous, periodic, and/or as-needed basis.The sensor data received in step 1455 may be used to determine whetherthere have been changes in the patient's respiratory distress (step1460). When the patient's respiratory condition does not improve, orworsens, step 1450 may be repeated with progressively more aggressiveand invasive treatment. When the patient's respiratory conditionimproves and/or when the respiratory distress of the patient ismoderate, rather than severe, the patient may be moved to the treatmentfacility/hospital floor (step 1465) where he or she may receive atreatment (1470) such as albuterol, oxygen gas, and/or HFNC. While onthe treatment facility/hospital floor, the patient may be monitored andsensor data may be received (step 1475) on a continuous, periodic,and/or as-needed basis and, when the respiratory distress is resolved,the patient may be discharged from the treatment facility. If thepatient's respiratory distress is not resolved (e.g., the respiratorydistress is the same or worse than a previously determined respiratorydistress indicator), then treatment 1470 may continue and, when thepatient's respiratory distress worsens to the point of being severe, heor she may be transferred to the intensive care unit (step 1445) and/orstep 1435 may be repeated.

In some embodiments, when a patient is being monitored for respiratorydistress using process 1400, a sensor array like sensor array 310 may beplaced on the patient as shown in FIG. 4C prior to step 1405 and thepatient may continuously wear the sensor array for a period of time whenhe or she is under treatment at the treatment facility. In this way,consistency of measurements may be achieved over time because differentsensors and/or different sensor placements are not impacting anydetermination of respiratory distress. Additionally, or alternatively,the determinations regarding whether the patient is in respiratorydistress of steps 1410, 1425, 1440, 1460, and/or 1480 may be made usingprocess 600 so that the output is a respiratory distress severity scoreand/or an indication of a degree of severity for TAA for the patient.

In one use case, the processes described herein may be used in theassessment and management of acute infantile bronchiolitis, the mostcommon cause of hospital admission in the first year of life. Atpresent, the current standard for monitoring infants admitted to, forexample, a hospital for bronchiolitis, is administration of a series ofregular and repeated assessments of the infant by trained clinicians aswell as the monitoring of respiratory rate, oxygen saturation, and signsof increased work of breathing, including thoracoabdominal asynchrony,nasal flaring, and accessory muscle use, among others. Oftentimes, theinfant must be continuously monitored to detect respiratorydeterioration that would otherwise go undetected with intermittentclinical assessment and thus progress to more severe disease. Thiscontinuous monitoring by trained clinical staff is laborious andexpensive in terms of cost and use of resources (e.g., the clinicalstaff). Further, direct observation and assessment of the infant issubject to errors caused by, for example, inter-observability andrelativistic assessments (as opposed to an absolute diagnosis orassessment).

The spectrum of oxygen and ventilatory support utilized in the treatmentof bronchiolitis, from least to most invasive, spans from supplementaloxygen (via nasal cannula or face mask) to high flow nasal cannula(HFNC) to continuous positive airway pressure (CPAP) to invasivemechanical ventilation in the most severe cases. Monitoring of breathingeffort via the systems and processes described herein would provideclinicians the ability to continuously monitor a patient withbronchiolitis without the need for continuous and direct observation andassessment of the patient. This has several advantages when comparedwith the current standard of care including, but not limited to, theability to passively, continuously, and consistently monitor the effortthe patient exerts while breathing so that changes (improvements ordeclines) may be accurately measured over time and treatment plans maybe adjusted accordingly. For example, information provided by thesystems and processes described herein (e.g., respiratory distressseverity score, degree of effort to breathe, etc.) may assist aclinician when making decisions regarding a severity of the patient'scondition or respiratory distress and/or decisions regarding theescalation and de-escalation of respiratory and/or ventilatory supportin this context.

In a prototypical use case in the management and/or treatment ofbronchiolitis, a patient with symptoms of respiratory distress presentsto a treatment facility (e.g., urgent care clinic, hospital, emergencydepartment of a hospital) where array 310 may be placed on a patient sothat sensor data may be received (step 1405 of process 1400). When thedegree of respiratory distress is minor (or inconclusive) (step 1410),treatment in the form of, for example, supplemental oxygen may beadministered (step 1415). Sensor data may again be received (step 1420)and if the patient is still in respiratory distress (step 1425), he orshe may be admitted to the general hospital ward (e.g., step 1430) forsuspected bronchiolitis. Alternatively, a patient may be directlyadmitted to the general hospital ward if the patient is observed to haveovert respiratory distress when entering the treatment facility (i.e.,process 1400 may start at step 1415 (e.g., when sensor data is notgathered because respiratory distress is readily observable) and/orprocess 1400 may start at step 1430). The patient may be continuouslymonitored (step 1425 and 1440) for respiratory distress via, forexample, execution of process 600. If serious respiratory distress isdetected and/or if there is moderate respiratory decline, an alarm maybe issued alerting the care team of the patient's condition. A clinicianmay then observe the patient to assess his or her condition and, ifnecessary, treatment provided to the patient may be adjusted (step 1450or 1470) (e.g., oxygen requirements of the patient and/or escalation ofrespiratory therapy (e.g., escalation to non-invasive ventilation suchas CPAP)). Intensive Care Unit admission may occur at this stage andcontinuous monitoring of the patient with the systems described hereinmay continue (steps 1445-1460).

Should the clinical team then receive an alarm from the systemsdescribed herein indicating severe respiratory distress and/or decline(step 1440 or 1460), mechanical ventilation may be considered,especially in setting of other indications for intubation such as poormental status, severe hypoxemia, or hypercapnia. Alternatively, shouldthe system indicate an improvement in respiratory status (e.g., step1460), the patient could be weaned from oxygen support therapy andpotentially transferred from the ICU to the floor of the hospital (step1465). The patient could then continue to be monitored (steps 1475-1480)until discharge, which would only occur once the sensor data, as well asphysical examination, indicate minimal work of breathing in the absenceof supportive therapy. This would translate to a near normal score ofrespiratory severity.

In another use case, the systems, devices, and processes describedherein may be used in the diagnosis and management (or treatment) ofCOVID-19 (or non-COVID-19) acute respiratory distress syndrome ARDS. Atpresent, there is conflicting evidence regarding the role of high-flownasal cannula (HFNC) and non-invasive ventilation (NIV) in the earlymanagement of COVID-19 respiratory distress. Some studies have found noevidence of increased mortality after delaying intubation in favor ofHFNC or NIV, suggesting that in less severe disease, such modalities canbe used to successfully treat the disease while avoiding the potentialfor injuries associated with invasive ventilation. Other studies havefound that failure to intubate early leads to increased mortality, owingto rapid deterioration and patient self-induced lung injury (P-SILI) dueto overly vigorous spontaneous ventilation. When these factors areconsidered with the risk of aerosolization of the virus with HFNC andNIV, thereby exposing bedside healthcare providers, early intubation maybe considered a preferred approach for the management of a respiratorydisease or infection like SARS, MERS, SARS-CoV-2 (i.e., COVID-19)respiratory distress. Continuous monitoring of breathing effort usingthe systems, devices, and processes described herein may providevaluable indications of respiratory distress and/or a degree of effortthe patient exerts to breathe, which may help guide early oxygenenrichment therapy and intubation strategy in patients with COVID-19. Inthis use case, a system and/or device as described herein may be placedupon a patient with either suspected or confirmed respiratory infectionwho is exhibiting observable signs of moderate respiratory distress,increased work of breathing by physical examination, and/or hypoxemiaupon hospital admission (step 1430 which may be performed with, orwithout, the sensor data received at step(s) 1405 and/or 1420). In theabsence of significant dyspnea or severe respiratory distress (step1440), the patient may initially be treated with a brief (less than 24hour) HFNC or non-invasive ventilation (NIV) (step 1470) while beingmonitored (continuously, periodically, and/or as-needed) with thesystems/devices described herein in order to determine a degree ofeffort the patient is exerting while breathing, a degree of respiratorydistress, and/or a respiratory distress score over time (step 1480). Thesystems and/or devices described herein may be used to determine thesuccess of the NIV trial (step 1480) via, for example, comparingrespiratory distress scores and/or respiratory effort determinationsover time to quantify improvement or further decline. If the patient'scondition stabilizes and/or improves (e.g., improving respiratorydistress scores or decreased effort to breathe) treatment may becontinued and/or de-escalated. If the patient's condition declinesand/or when further decompensation is indicated, escalation to moreaggressive and/or invasive treatment (e.g., mechanical ventilation)and/or admission to the ICU (step 1480) may be warranted.

A standard of care is to begin the process of weaning a patient frommechanical ventilation as soon as 24-hours after intubation providedthat the patient can breathe at least somewhat on his or her own.Ventilator modes that allow for spontaneous breathing, whether assistedor unassisted, may facilitate this process. However, weaning a patientfrom a ventilator poses dangers/risks to the patient as may occur whenhigh respiratory efforts lead to uncontrolled transpulmonary pressuresand leave the patient at risk of P-SILI and weaning failure. Thedescribed system can be used to ensure adequately minimized effortsduring spontaneous breathing in mechanical ventilation by monitoring theeffort the patient is exerting while breathing so that adequateadjustments and/or countermeasures may be taken with, for example,ventilation equipment to reduce risks to the patient. For example, ifthe patient's breathing effort is determined to be higher than desired(e.g., a respiratory effort score that is above a desired value orthreshold) by the clinical care team, the ventilator mode may beadjusted to controlled ventilation, where the patient's respiration iscompletely controlled by the ventilator, which may serve to decrease theamount of effort the patient is exerting while breathing. On the otherhand, there is increasing evidence that insufficient patient effortduring mechanical ventilation has been associated with atrophicdiaphragm injury due to muscle inactivity. For this reason, the systemcan also be used to ensure adequately elevated breathing efforts (e.g.,a respiratory effort score that is above a desired value or threshold)by the patient are being exerted. If the effort the patient is exertingto breathe is insufficient (a respiratory effort score that is below adesired value or threshold), in response to this insufficient effort, acare team can adjust ventilator settings to allow for greaterspontaneous breathing and less assisted breaths.

We claim:
 1. A method comprising: receiving, by a processor, a first setof sensor data from a first sensor positioned on the epidermis of apatient being monitored for respiratory distress syndrome in a firstlocation, the first sensor being in communication with the processor;receiving, by the processor, a second set of sensor data from a secondsensor positioned on the epidermis of the patient in a second location,the second sensor being in communication with the processor;determining, by the processor, a phase difference between the first andsecond sets of sensor data; determining, by the processor, a degree ofrespiratory effort exhibited by the patient over a time period based ona determined phase difference between the first and second sets ofsensor data; determining, by the processor, a change in the patient'srespiratory distress syndrome over the time period responsively to thedetermined degree of respiratory effort exhibited by the patient overthe time period; and communicating, by the processor, the change in thepatient's respiratory distress syndrome to a display device.
 2. Themethod of claim 1, wherein the determination of the degree ofrespiratory effort exhibited by the patient includes determining adegree of thoraco-abdominal asynchrony (TAA) exhibited by the patient.3. The method of claim 1, wherein the first and second sensors areaccelerometers and the first and second sets of sensor data includeacceleration measurements.
 4. The method of claim 1, wherein the firstand second sensors are force meters and the first and second sets ofsensor data include force measurements.
 5. The method of claim 1,wherein the first and second sensors are strain sensors and the firstand second sets of sensor data include strain measurements.
 6. Themethod of claim 1, further comprising: receiving an indication of arespiratory rate of the patient, wherein the determination of the degreeof respiratory effort exhibited by the patient is further based on therespiratory rate.
 7. The method of claim 1, further comprising:receiving a third set of sensor data from a third sensor positioned onthe epidermis of the patient in a third location, the third sensor beingin communication with the processor; determining a phase differencebetween at least one of the first and third sets of sensor data and thesecond and third sets of sensor data, wherein determining the degree ofrespiratory effort exhibited by the patient is further based on adetermined phase difference between the first and third sets of sensordata and the second and third sets of sensor data.
 8. The method ofclaim 1, further comprising: performing, by the processor,cross-correlation analysis between the first and second sets of sensordata prior to determining the degree of respiratory effort exhibited bythe patient, wherein the degree of respiratory effort exhibited by thepatient is further based on a result of the cross-correlation analysis.9. The method of claim 8, wherein the first and second sets of sensordata are a signal collected over the time period, the method furthercomprising: mapping a result of a cross-correlation calculation at aparticular time during the time period with a maximum cross-correlationvalue calculated during the time period prior to the determination ofthe degree of respiratory effort exhibited by the patient.
 10. Themethod of claim 1, further comprising: receiving an indication of arespiratory rate variability of the patient, wherein the determinationof the degree of respiratory effort exhibited by the patient is furtherbased on respiratory rate variability.
 11. A method comprising:receiving, by a processor, a first set of sensor data from a firstsensor positioned on the epidermis of a patient being monitored forrespiratory distress syndrome in a first location, the first sensorbeing in communication with the processor; receiving, by the processor,a second set of sensor data from a second sensor positioned on theepidermis of the patient in a second location, the second sensor beingin communication with the processor; performing, by the processor,cross-correlation analysis between the first and second sets of sensordata; determining, by the processor, a degree of respiratory effortexhibited by the patient over a time period based on a result of thecross-correlation analysis; determining, by the processor, a change inthe patient's respiratory distress syndrome over the time periodresponsively to the determined degree of respiratory effort exhibited bythe patient over the time period; and communicating, by the processor,the change in the patient's respiratory distress syndrome to a displaydevice.
 12. The method of claim 11, wherein the determination of thedegree of respiratory effort exhibited by the patient includesdetermining a degree of thoraco-abdominal asynchrony (TAA) exhibited bythe patient.
 13. The method of claim 11, wherein the first and secondsensors are accelerometers and the first and second sets of sensor datainclude acceleration measurements.
 14. The method of claim 11, whereinthe first and second sensors are force meters and the first and secondsets of sensor data include force measurements.
 15. The method of claim11, wherein the first and second sensors are strain sensors and thefirst and second sets of sensor data include strain measurements. 16.The method of claim 11, further comprising: receiving an indication of arespiratory rate of the patient, wherein the determination of the degreeof respiratory effort exhibited by the patient is further based on therespiratory rate.
 17. The method of claim 11, further comprising:determining, by the processor, a phase difference between the first andsecond sets of sensor data prior to determining the degree ofrespiratory effort exhibited by the patient, wherein the degree ofrespiratory effort exhibited by the patient is further based on adetermined phase difference.
 18. The method of claim 17, wherein thefirst and second sets of sensor data are a signal collected over thetime period, the method further comprising: mapping a result of across-correlation calculation at a particular time during the timeperiod with a maximum cross-correlation value calculated during the timeperiod prior to the determination of the degree of respiratory effortexhibited by the patient.
 19. The method of claim 11, furthercomprising: receiving a third set of sensor data from the third sensorpositioned on the epidermis of the patient in a third location, thethird sensor being in communication with the processor; performing, bythe processor, cross-correlation analysis between the first and thirdsets of data or the second and third sets of sensor data, whereindetermining the degree of respiratory effort exhibited by the patient isfurther based on a result of the cross-correlation analysis between thefirst and third sets of sensor data or the second and third sets ofsensor data.
 20. The method of claim 11, further comprising: receivingan indication of a respiratory rate variability of the patient, whereinthe determination of the degree of respiratory effort exhibited by thepatient is further based on respiratory rate variability.